Psychomedical named entity recognition method based on multi-level feature extraction and multi-granularity embedding fusion

被引：0

作者：

Zixuan Liu ^{[1
]}

Guofang Zhang ^{[1
]}

Yanguang Shen ^{[2
]}

机构：

[1] Hebei University,School of Cyber Security and Computer

[2] Hebei University of Engineering,School of Information Science and Electrical Engineering

来源：

Scientific Reports | / 15卷 / 1期

关键词：

Named entity recognition; Psychological medicine; Multi-granularity fusion; GA-FNNAttention mechanism; MFE-BERT model;

D O I：

10.1038/s41598-025-90939-8

中图分类号：

学科分类号：

摘要：

Named Entity Recognition (NER) in psychomedicine is one of the key tasks in natural language processing in psychomedicine. It aims to identify and classify specialized terms in psychomedical texts and provide powerful support for downstream tasks. Psychological medicine texts are characterized by long paragraphs, complex sentences, and scattered knowledge. The current character-based psychomedicine NER model has single embedded information. It lacks structural and phonetic characterization information. Migrating NER models from the general purpose domain to the psychomedical domain are not effective in improving entity recognition accuracy. To solve this problem, we propose a NER method based on multi-level feature extraction and multi-granularity embedding fusion (MFME-NER), which aims to provide an innovative solution. First, three different granularities of embedding information, character embedding, radical embedding and pinyin embedding, are introduced to enrich the semantic representation of the input text. Second, the BERT model is improved. Merging the features of all Encoder layers inside the output. So that the BERT model has multi-layer feature extraction capability (MFE-BERT). The character embedding is pre-trained by MFE-BERT. And the BiLSTM model is utilized for the extraction of features at the character granularity. The features of radical embedding and pinyin embedding are extracted separately by the CNN model, and then feature fusion is performed. Finally, feature vectors at three granularities are integrated using a gated feed-forward neural network attention mechanism (GA-FNNAtention). The experimental results show that MFME-NER achieved 94.26% and 89.63% F1 Score in the self-constructed psychomedical dataset PsyDatase and CBLUE dataset, respectively. The proposed method surpasses the currently used evaluation metrics, thus substantiating its rationality and efficacy.This study can better contribute to the analysis of psychomedical data.

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